Root hairs are tubular outgrowths specifically differentiated from epidermal cells in a differentiation zone. The formation of root hairs greatly increases the surface area of a root and maximizes its ability to absor...Root hairs are tubular outgrowths specifically differentiated from epidermal cells in a differentiation zone. The formation of root hairs greatly increases the surface area of a root and maximizes its ability to absorb water and inorganic nutrients essential for plant growth and development. Root hair development is strictly regulated by intracellular and intercellular signal communications. Cell surface-localized receptor-like protein kinases （P, LKs） have been shown to be important components in these cellular processes, tn this review, the functions of a number of key P, LKs in regulating Arabidopsis root hair development are discussed, especially those involved in root epidermal cell fate determination and root hair tip growth.展开更多
为研究肉骨粉部分替代鱼粉(添加或不添加晶体氨基酸)对大菱鲆(Scophthalmus maximus L.)幼鱼的氨基酸应答(AAR)信号通路中关键调控因子基因表达量的影响。设计了3种等氮等能的饲料:鱼粉对照组(FM,60%鱼粉),肉骨粉替代组(MBM,肉骨粉替代...为研究肉骨粉部分替代鱼粉(添加或不添加晶体氨基酸)对大菱鲆(Scophthalmus maximus L.)幼鱼的氨基酸应答(AAR)信号通路中关键调控因子基因表达量的影响。设计了3种等氮等能的饲料:鱼粉对照组(FM,60%鱼粉),肉骨粉替代组(MBM,肉骨粉替代45%鱼粉蛋白),肉骨粉替代添加晶体必需氨基酸组(MBM+AA,肉骨粉替代45%鱼粉蛋白,添加晶体必需氨基酸至鱼粉组必需氨基酸水平)。实验选取初始体质量(9.01±0.01)g的大菱鲆,分别饱食投喂3种不同的饲料30天,检测肌肉和肠道中AAR信号通路中关键调控因子的基因表达量。研究表明:与FM组相比,MBM组显著提高了大菱鲆幼鱼肌肉中谷氨酰胺合成酶(Asparagine synthesis,ASNS)、转录激活因子3(Activating transcription factor3,ATF3)、转录激活因子4(Activating transcription factor4,ATF4)、CCAAT增强子结合蛋白(CCAAT-enhance binding protein homology protein,CHOP)、发育和DNA损伤应答调节因子1(Regulated in development and DNA damage responses1,REDD1)和真核翻译起始因子4E结合蛋白1(Eukaryotic initiation factor 4Ebinding protein 1,4E-BP1)表达量的峰值。在摄食后2h,MBM+AA组的ASNS、ATF3、ATF4、CHOP、REDD1和4E-BP1表达量峰值与FM组的表达量峰值无显著性差异,却显著低于MBM组的表达量峰值。3个处理组肌肉中酵母转录激活因子2(General control nonderepressible 2,GCN2)的基因表达量无显著性差异。同时,与FM组相比,MBM组显著上调肠道中GCN2、ASNS、ATF4、CHOP和4EBP1表达量的峰值,MBM+AA组对于降低这些基因的表达量无显著性效果。研究结果表明:肉骨粉替代45%鱼粉蛋白上调了肌肉和肠道中AAR信号通路中关键因子的表达,添加晶体氨基酸可以在一定程度上改善肉骨粉替代鱼粉对于肌肉中AAR信号通路中关键调控因子表达量的上调作用。展开更多
Gene regulatory network (GRN) inference from gene expression data is asignificant approach to understanding aspects of the biological system.Compared with generalized correlation-based methods, causality-inspiredones ...Gene regulatory network (GRN) inference from gene expression data is asignificant approach to understanding aspects of the biological system.Compared with generalized correlation-based methods, causality-inspiredones seem more rational to infer regulatory relationships. We proposeGRINCD, a novel GRN inference framework empowered by graph representationlearning and causal asymmetric learning, considering both linearand non-linear regulatory relationships. First, high-quality representation ofeach gene is generated using graph neural network. Then, we apply theadditive noise model to predict the causal regulation of each regulator-targetpair. Additionally, we design two channels and finally assemble them forrobust prediction. Through comprehensive comparisons of our frameworkwith state-of-the-art methods based on different principles on numerousdatasets of diverse types and scales, the experimental results show that ourframework achieves superior or comparable performance under variousevaluation metrics. Our work provides a new clue for constructing GRNs,and our proposed framework GRINCD also shows potential in identifyingkey factors affecting cancerdevelopment.展开更多
基金supported by grants from the National Natural Science Foundation of China(31700245 to Zhuoyun Wei,31720103902,31470380,and 31530005 to Jia Li)the China Postdoctoral Science Foundation(2018T111116 and 2016M602889 to Zhuoyun Wei)
文摘Root hairs are tubular outgrowths specifically differentiated from epidermal cells in a differentiation zone. The formation of root hairs greatly increases the surface area of a root and maximizes its ability to absorb water and inorganic nutrients essential for plant growth and development. Root hair development is strictly regulated by intracellular and intercellular signal communications. Cell surface-localized receptor-like protein kinases （P, LKs） have been shown to be important components in these cellular processes, tn this review, the functions of a number of key P, LKs in regulating Arabidopsis root hair development are discussed, especially those involved in root epidermal cell fate determination and root hair tip growth.
文摘为研究肉骨粉部分替代鱼粉(添加或不添加晶体氨基酸)对大菱鲆(Scophthalmus maximus L.)幼鱼的氨基酸应答(AAR)信号通路中关键调控因子基因表达量的影响。设计了3种等氮等能的饲料:鱼粉对照组(FM,60%鱼粉),肉骨粉替代组(MBM,肉骨粉替代45%鱼粉蛋白),肉骨粉替代添加晶体必需氨基酸组(MBM+AA,肉骨粉替代45%鱼粉蛋白,添加晶体必需氨基酸至鱼粉组必需氨基酸水平)。实验选取初始体质量(9.01±0.01)g的大菱鲆,分别饱食投喂3种不同的饲料30天,检测肌肉和肠道中AAR信号通路中关键调控因子的基因表达量。研究表明:与FM组相比,MBM组显著提高了大菱鲆幼鱼肌肉中谷氨酰胺合成酶(Asparagine synthesis,ASNS)、转录激活因子3(Activating transcription factor3,ATF3)、转录激活因子4(Activating transcription factor4,ATF4)、CCAAT增强子结合蛋白(CCAAT-enhance binding protein homology protein,CHOP)、发育和DNA损伤应答调节因子1(Regulated in development and DNA damage responses1,REDD1)和真核翻译起始因子4E结合蛋白1(Eukaryotic initiation factor 4Ebinding protein 1,4E-BP1)表达量的峰值。在摄食后2h,MBM+AA组的ASNS、ATF3、ATF4、CHOP、REDD1和4E-BP1表达量峰值与FM组的表达量峰值无显著性差异,却显著低于MBM组的表达量峰值。3个处理组肌肉中酵母转录激活因子2(General control nonderepressible 2,GCN2)的基因表达量无显著性差异。同时,与FM组相比,MBM组显著上调肠道中GCN2、ASNS、ATF4、CHOP和4EBP1表达量的峰值,MBM+AA组对于降低这些基因的表达量无显著性效果。研究结果表明:肉骨粉替代45%鱼粉蛋白上调了肌肉和肠道中AAR信号通路中关键因子的表达,添加晶体氨基酸可以在一定程度上改善肉骨粉替代鱼粉对于肌肉中AAR信号通路中关键调控因子表达量的上调作用。
文摘Gene regulatory network (GRN) inference from gene expression data is asignificant approach to understanding aspects of the biological system.Compared with generalized correlation-based methods, causality-inspiredones seem more rational to infer regulatory relationships. We proposeGRINCD, a novel GRN inference framework empowered by graph representationlearning and causal asymmetric learning, considering both linearand non-linear regulatory relationships. First, high-quality representation ofeach gene is generated using graph neural network. Then, we apply theadditive noise model to predict the causal regulation of each regulator-targetpair. Additionally, we design two channels and finally assemble them forrobust prediction. Through comprehensive comparisons of our frameworkwith state-of-the-art methods based on different principles on numerousdatasets of diverse types and scales, the experimental results show that ourframework achieves superior or comparable performance under variousevaluation metrics. Our work provides a new clue for constructing GRNs,and our proposed framework GRINCD also shows potential in identifyingkey factors affecting cancerdevelopment.
